Multi-level refined power system operation mode analysis: A data-driven approach

The high renewable penetration will cause the power system operation mode (PSOM) to change frequently. At present, the selection of PSOM mainly depends on the experience of relevant staff. However, selecting PSOM based on experience is difficult to comprehensively and elaborately analyze the possible PSOMs within a dispatching cycle, which inevitably invites troubles to the power system planning and dispatching. In this paper, a data-driven analysis method is proposed, which integrates the techniques of preprocessing, dimensionality reduction, clustering, and data visualization of high-dimensional data sets of power system operation. Simultaneously, qualitative and quantitative methods are used to analyze, evaluate the PSOM and their development laws. Finally, to verify the effectiveness of the proposed multi-level refinement extraction method of PSOM, the normal power system operation mode (NPSOM), transition power system operation mode (TPSOM), extreme power system operation mode (EPSOM) are obtained and analyzed according to the real operation data of the urban power system. The abundant analysis results show that the proposed fine-grained extraction method of PSOM is compelling, facilitating the power grid enterprise operation and planning.

Automated summary

This paper proposes a data-driven method for analyzing and evaluating the operation modes of power systems. By integrating techniques such as preprocessing, dimensionality reduction, clustering, and data visualization, high-dimensional datasets of power system operation can be processed and analyzed. Qualitative and quantitative methods are used to evaluate and study different operation modes in order to improve the efficiency of power grid enterprise operation and planning.